Abstract
Epistatic interactions among multiple genetic variants in the human genome may be important in determining individual susceptibility to common diseases. Although some existing computational methods for identifying genetic interactions have been effective for small-scale studies, we here propose a method, denoted 'bayesian epistasis association mapping' (BEAM), for genome-wide case-control studies. BEAM treats the disease-associated markers and their interactions via a bayesian partitioning model and computes, via Markov chain Monte Carlo, the posterior probability that each marker set is associated with the disease. Testing this on an age-related macular degeneration genome-wide association data set, we demonstrate that the method is significantly more powerful than existing approaches and that genome-wide case-control epistasis mapping with many thousands of markers is both computationally and statistically feasible.
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Acknowledgements
This work was supported in part by US National Institutes of Health grant R01HG002518-06, US National Science Foundation grant DMS-0204674 and a grant from the National Science Foundation of China (10228102). We thank J. Hoh for providing us the AMD data set and T. Niu for discussions.
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Y.Z. and J.S.L. designed the statistical models and simulation studies together. Y.Z. implemented the method and wrote the software. Both authors contributed to the writing of the manuscript.
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Supplementary Methods, Supplementary Figures 1–4, Supplementary Table 1 (PDF 367 kb)
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Zhang, Y., Liu, J. Bayesian inference of epistatic interactions in case-control studies. Nat Genet 39, 1167–1173 (2007). https://doi.org/10.1038/ng2110
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DOI: https://doi.org/10.1038/ng2110
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